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Optimization of Mixed Control Supply Chain Logistics Planning Under Uncertain Environment

In: Optimization of Integrated Supply Chain Planning under Multiple Uncertainty

Author

Listed:
  • Juping Shao

    (Suzhou University of Science and Technology)

  • Yanan Sun

    (Suzhou Industrial Park Anwood Logistics System Co., Ltd)

  • Bernd Noche

    (University Duisburg-Essen)

Abstract

Usually, the demand is described with random variables under uncertain environment. When we describe the demand with variables, we need a great amount of empirical statistics to get the distribution function. However, these data might be hard to get in some cases. The fussy sets theory is then a commonly used and effective method which can quantifiably describe the uncertain demand. The membership function of fuzzy numbers can be determined by the decision makers when using fuzzy numbers to describe the demand, which is much easier to determine the membership function than the determination of distribution functions of random variable.

Suggested Citation

  • Juping Shao & Yanan Sun & Bernd Noche, 2015. "Optimization of Mixed Control Supply Chain Logistics Planning Under Uncertain Environment," Springer Books, in: Optimization of Integrated Supply Chain Planning under Multiple Uncertainty, edition 127, chapter 0, pages 149-183, Springer.
    • Handle: RePEc:spr:sprchp:978-3-662-47250-7_6
    DOI: 10.1007/978-3-662-47250-7_6
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    Cited by:

    1. Khishtandar, Soheila, 2019. "Simulation based evolutionary algorithms for fuzzy chance-constrained biogas supply chain design," Applied Energy, Elsevier, vol. 236(C), pages 183-195.

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